Pagerank

The Anatomy of a Large-Scale
Hypertextual Web Search Engine
Sergey Brin and Lawrence Page
Computer Science Department,
Stanford University, Stanford, CA 94305, USA
sergey@cs.stanford.edu and page@cs.stanford.edu
Abstract
In this paper, we present Google, a prototype of a large-scale search engine
which makes heavy use of the structure present in hypertext. Google is
designed to crawl and index the Web efficiently and produce much more
satisfying search results than existing systems. The prototype with a full
text and hyperlink database of at least 24 million pages is available at
http://
google.stanford.edu/ To engineer a search engine is a challenging task.
Search engines index tens to hundreds of millions of web pages involving a
comparable number of distinct terms. They answer tens of millions of
queries every day. Despite the importance of large-scale search engines on
the web, very little academic research has been done on them.
Furthermore, due to rapid advance in technology and web proliferation,
creating a web search engine today is very different from three years ago.
This paper provides an in-depth description of our large-scale web search
engine -- the first such detailed public description we know of to date.
Apart from the problems of scaling traditional search techniques to data of
this magnitude, there are new technical challenges involved with using the
additional information present in hypertext to produce better search
results. This paper addresses this question of how to build a practical large-
scale system which can exploit the additional information present in
hypertext. Also we look at the problem of how to effectively deal with
uncontrolled hypertext collections where anyone can publish anything
they want.

Keywords
World Wide Web, Search Engines, Information Retrieval, PageRank, Google

1. Introduction
(Note: There are two versions of this paper -- a longer full version and a shorter
printed version. The full version is available on the web and the conference CD-
ROM.)
The web creates new challenges for information retrieval. The amount of
information on the web is growing rapidly, as well as the number of new users
inexperienced in the art of web research. People are likely to surf the web using its
link graph, often starting with high quality human maintained indices such as
http://www7.scu.edu.au/programme/fullpapers/1921/com1921.htm 1

The web creates new challenges for information retrieval. The amount of
information on the web is growing rapidly, as well as the number of new users
inexperienced in the art of web research. People are likely to surf the web using its
link graph, often starting with high quality human maintained indices such as
Yahoo! or with search engines. Human maintained lists cover popular topics
effectively but are subjective, expensive to build and maintain, slow to improve,
and cannot cover all esoteric topics. Automated search engines that rely on
keyword matching usually return too many low quality matches. To make matters
worse, some advertisers attempt to gain people's attention by taking measures
meant to mislead automated search engines. We have built a large-scale search
engine which addresses many of the problems of existing systems. It makes
especially heavy use of the additional structure present in hypertext to provide
much higher quality search results. We chose our system name, Google, because it
100
is a common spelling of googol, or 10 and fits well with our goal of building very
large-scale search engines.
1.1 Web Search Engines -- Scaling Up: 1994 - 2000
Search engine technology has had to scale dramatically to keep up with the
growth of the web. In 1994, one of the first web search engines, the World Wide
Web Worm (WWWW)[McBryan 94] had an index of 110,000 web pages and web
accessible documents. As of November, 1997, the top search engines claim to
index from 2 million (WebCrawler) to 100 million web documents (from Search
Engine Watch). It is foreseeable that by the year 2000, a comprehensive index of
the Web will contain over a billion documents. At the same time, the number of
queries search engines handle has grown incredibly too. In March and April 1994,
the World Wide Web Worm received an average of about 1500 queries per day. In
November 1997, Altavista claimed it handled roughly 20 million queries per day.
With the increasing number of users on the web, and automated systems which
query search engines, it is likely that top search engines will handle hundreds of
millions of queries per day by the year 2000. The goal of our system is to address
many of the problems, both in quality and scalability, introduced by scaling search
engine technology to such extraordinary numbers.
1.2. Google: Scaling with the Web
Creating a search engine which scales even to today's web presents many
challenges. Fast crawling technology is needed to gather the web documents and
keep them up to date. Storage space must be used efficiently to store indices and,
optionally, the documents themselves. The indexing system must process
hundreds of gigabytes of data efficiently. Queries must be handled quickly, at a
rate of hundreds to thousands per second.
These tasks are becoming increasingly difficult as the Web grows. However,
hardware performance and cost have improved dramatically to partially offset the
difficulty. There are, however, several notable exceptions to this progress such as
disk seek time and operating system robustness. In designing Google, we have
considered both the rate of growth of the Web and technological changes. Google
is designed to scale well to extremely large data sets. It makes efficient use of
storage space to store the index. Its data structures are optimized for fast and
efficient access (see section

hardware performance and cost have improved dramatically to partially offset the
difficulty. There are, however, several notable exceptions to this progress such as
disk seek time and operating system robustness. In designing Google, we have
considered both the rate of growth of the Web and technological changes. Google
is designed to scale well to extremely large data sets. It makes efficient use of
storage space to store the index. Its data structures are optimized for fast and
efficient access (see section 4.2). Further, we expect that the cost to index and
store text or HTML will eventually decline relative to the amount that will be
available (see Appendix B). This will result in favorable scaling properties for
centralized systems like Google.
1.3 Design Goals
1.3.1 Improved Search Quality
Our main goal is to improve the quality of web search engines. In 1994, some
people believed that a complete search index would make it possible to find
anything easily. According to Best of the Web 1994 -- Navigators, "The best
navigation service should make it easy to find almost anything on the Web (once
all the data is entered)." However, the Web of 1997 is quite different. Anyone who
has used a search engine recently, can readily testify that the completeness of the
index is not the only factor in the quality of search results. "Junk results" often
wash out any results that a user is interested in. In fact, as of November 1997, only
one of the top four commercial search engines finds itself (returns its own search
page in response to its name in the top ten results). One of the main causes of this
problem is that the number of documents in the indices has been increasing by
many orders of magnitude, but the user's ability to look at documents has not.
People are still only willing to look at the first few tens of results. Because of this, as
the collection size grows, we need tools that have very high precision (number of
relevant documents returned, say in the top tens of results). Indeed, we want our
notion of "relevant" to only include the very best documents since there may be
tens of thousands of slightly relevant documents. This very high precision is
important even at the expense of recall (the total number of relevant documents
the system is able to return). There is quite a bit of recent optimism that the use of
more hypertextual information can help improve search and other applications
[Marchiori 97] [Spertus 97] [Weiss 96] [Kleinberg 98]. In particular, link structure
[Page 98] and link text provide a lot of information for making relevance judgments
and quality filtering. Google makes use of both link structure and anchor text (see
Sections 2.1 and 2.2).
1.3.2 Academic Search Engine Research
Aside from tremendous growth, the Web has also become increasingly commercial
over time. In 1993, 1.5% of web servers were on .com domains. This number grew
to over 60% in 1997. At the same time, search engines have migrated from the
academic domain to the commercial. Up until now most search engine
development has gone on at companies with little publication of technical details.
This causes search engine technology to remain largely a black art and to be
advertising oriented (see Appendix A). With Google, we have a strong goal to push
more development and understanding into the academic realm.
Another important design goal was to build systems that reasonable numbers of
http://www7.scu.edu.au/programme/fullpapers/1921/com1921.htm
people can actually use. Usage was important to us because we think some of the3
most interesting research will involve leveraging the vast amount of usage data
that is available from modern web systems. For example, there are many tens of
millions of searches performed every day. However, it is very difficult to get this

Another important design goal was to build systems that reasonable numbers of
people can actually use. Usage was important to us because we think some of the
most interesting research will involve leveraging the vast amount of usage data
that is available from modern web systems. For example, there are many tens of
millions of searches performed every day. However, it is very difficult to get this
data, mainly because it is considered commercially valuable.
Our final design goal was to build an architecture that can support novel research
activities on large-scale web data. To support novel research uses, Google stores
all of the actual documents it crawls in compressed form. One of our main goals in
designing Google was to set up an environment where other researchers can
come in quickly, process large chunks of the web, and produce interesting results
that would have been very difficult to produce otherwise. In the short time the
system has been up, there have already been several papers using databases
generated by Google, and many others are underway. Another goal we have is to
set up a Spacelab-like environment where researchers or even students can
propose and do interesting experiments on our large-scale web data.

2. System Features
The Google search engine has two important features that help it produce high
precision results. First, it makes use of the link structure of the Web to calculate a
quality ranking for each web page. This ranking is called PageRank and is
described in detail in [Page 98]. Second, Google utilizes link to improve search
results.
2.1 PageRank: Bringing Order to the Web
The citation (link) graph of the web is an important resource that has largely gone
unused in existing web search engines. We have created maps containing as many
as 518 million of these hyperlinks, a significant sample of the total. These maps
allow rapid calculation of a web page's "PageRank", an objective measure of its
citation importance that corresponds well with people's subjective idea of
importance. Because of this correspondence, PageRank is an excellent way to
prioritize the results of web keyword searches. For most popular subjects, a simple
text matching search that is restricted to web page titles performs admirably when
PageRank prioritizes the results (demo available atgoogle.stanford.edu). For the
type of full text searches in the main Google system, PageRank also helps a great
deal.
2.1.1 Description of PageRank Calculation
Academic citation literature has been applied to the web, largely by counting
citations or backlinks to a given page. This gives some approximation of a page's
importance or quality. PageRank extends this idea by not counting links from all
pages equally, and by normalizing by the number of links on a page. PageRank is
defined as follows:

We assume page A has pages T1...Tn which point to it (i.e., are citations).
The parameter d is a damping factor which can be set between 0 and 1.
We usually set d to 0.85. There are more details about d in the next
section. Also C(A) is defined as the number of links going out of page A.
The PageRank of a page A is given as follows:
PR(A) = (1-d) + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))
Note that the PageRanks form a probability distribution over web pages,
so the sum of all web pages' PageRanks will be one.
PageRank or PR(A) can be calculated using a simple iterative algorithm, and
corresponds to the principal eigenvector of the normalized link matrix of the web.
Also, a PageRank for 26 million web pages can be computed in a few hours on a
medium size workstation. There are many other details which are beyond the
scope of this paper.
2.1.2 Intuitive Justification
PageRank can be thought of as a model of user behavior. We assume there is a
"random surfer" who is given a web page at random and keeps clicking on links,
never hitting "back" but eventually gets bored and starts on another random
page. The probability that the random surfer visits a page is its PageRank. And, the
d damping factor is the probability at each page the "random surfer" will get
bored and request another random page. One important variation is to only add
the damping factor d to a single page, or a group of pages. This allows for
personalization and can make it nearly impossible to deliberately mislead the
system in order to get a higher ranking. We have several other extensions to
PageRank, again see [Page 98].
Another intuitive justification is that a page can have a high PageRank if there are
many pages that point to it, or if there are some pages that point to it and have a
high PageRank. Intuitively, pages that are well cited from many places around the
web are worth looking at. Also, pages that have perhaps only one citation from
something like the Yahoo! homepage are also generally worth looking at. If a page
was not high quality, or was a broken link, it is quite likely that Yahoo's homepage
would not link to it. PageRank handles both these cases and everything in
between by recursively propagating weights through the link structure of the
web.
2.2 Anchor Text
The text of links is treated in a special way in our search engine. Most search
engines associate the text of a link with the page that the link is on. In addition, we
associate it with the page the link points to. This has several advantages. First,
anchors often provide more accurate descriptions of web pages than the pages
themselves. Second, anchors may exist for documents which cannot be indexed
by a text-based search engine, such as images, programs, and databases. This
makes it possible to return web pages which have not actually been crawled. Note
that pages that have not been crawled can cause problems, since they are never
checked for validity before being returned to the user. In this case, the search
engine can even return a page that never actually existed, but had hyperlinks

engines associate the text of a link with the page that the link is on. In addition, we
associate it with the page the link points to. This has several advantages. First,
anchors often provide more accurate descriptions of web pages than the pages
themselves. Second, anchors may exist for documents which cannot be indexed
by a text-based search engine, such as images, programs, and databases. This
makes it possible to return web pages which have not actually been crawled. Note
that pages that have not been crawled can cause problems, since they are never
checked for validity before being returned to the user. In this case, the search
engine can even return a page that never actually existed, but had hyperlinks
pointing to it. However, it is possible to sort the results, so that this particular
problem rarely happens.
This idea of propagating anchor text to the page it refers to was implemented in
the World Wide Web Worm [ cBryan 94] especially because it helps search non-
M
text information, and expands the search coverage with fewer downloaded
documents. We use anchor propagation mostly because anchor text can help
provide better quality results. Using anchor text efficiently is technically difficult
because of the large amounts of data which must be processed. In our current
crawl of 24 million pages, we had over 259 million anchors which we indexed.
2.3 Other Features
Aside from PageRank and the use of anchor text, Google has several other
features. First, it has location information for all hits and so it makes extensive use
of proximity in search. Second, Google keeps track of some visual presentation
details such as font size of words. Words in a larger or bolder font are weighted
higher than other words. Third, full raw HTML of pages is available in a repository.

3 Related Work
Search research on the web has a short and concise history. The World Wide Web
Worm (WWWW) [McBryan 94] was one of the first web search engines. It was
subsequently followed by several other academic search engines, many of which
are now public companies. Compared to the growth of the Web and the
importance of search engines there are precious few documents about recent
search engines [Pinkerton 94]. According to Michael Mauldin (chief scientist, Lycos
Inc) [Mauldin], "the various services (including Lycos) closely guard the details of
these databases". However, there has been a fair amount of work on specific
features of search engines. Especially well represented is work which can get
results by post-processing the results of existing commercial search engines, or
produce small scale "individualized" search engines. Finally, there has been a lot of
research on information retrieval systems, especially on well controlled collections.
In the next two sections, we discuss some areas where this research needs to be
extended to work better on the web.
3.1 Information Retrieval
Work in information retrieval systems goes back many years and is well developed
[Witten 94]. However, most of the research on information retrieval systems is on
small well controlled homogeneous collections such as collections of scientific
papers or news stories on a related topic. Indeed, the primary benchmark for
information retrieval, the Text Retrieval Conference TREC 96], uses a fairly small, 6
[
well controlled collection for their benchmarks. The "Very Large Corpus"
benchmark is only 20GB compared to the 147GB from our crawl of 24 million web
pages. Things that work well on TREC often do not produce good results on the

]. However, most of the research on information retrieval systems is on
small well controlled homogeneous collections such as collections of scientific
papers or news stories on a related topic. Indeed, the primary benchmark for
information retrieval, the Text Retrieval Conference TREC 96], uses a fairly small,
[
well controlled collection for their benchmarks. The "Very Large Corpus"
benchmark is only 20GB compared to the 147GB from our crawl of 24 million web
pages. Things that work well on TREC often do not produce good results on the
web. For example, the standard vector space model tries to return the document
that most closely approximates the query, given that both query and document
are vectors defined by their word occurrence. On the web, this strategy often
returns very short documents that are the query plus a few words. For example,
we have seen a major search engine return a page containing only "Bill Clinton
Sucks" and picture from a "Bill Clinton" query. Some argue that on the web, users
should specify more accurately what they want and add more words to their
query. We disagree vehemently with this position. If a user issues a query like "Bill
Clinton" they should get reasonable results since there is a enormous amount of
high quality information available on this topic. Given examples like these, we
believe that the standard information retrieval work needs to be extended to deal
effectively with the web.
3.2 Differences Between the Web and Well Controlled Collections
The web is a vast collection of completely uncontrolled heterogeneous
documents. Documents on the web have extreme variation internal to the
documents, and also in the external meta information that might be available. For
example, documents differ internally in their language (both human and
programming), vocabulary (email addresses, links, zip codes, phone numbers,
product numbers), type or format (text, HTML, PDF, images, sounds), and may even
be machine generated (log files or output from a database). On the other hand, we
define external meta information as information that can be inferred about a
document, but is not contained within it. Examples of external meta information
include things like reputation of the source, update frequency, quality, popularity
or usage, and citations. Not only are the possible sources of external meta
information varied, but the things that are being measured vary many orders of
magnitude as well. For example, compare the usage information from a major
homepage, like Yahoo's which currently receives millions of page views every day
with an obscure historical article which might receive one view every ten years.
Clearly, these two items must be treated very differently by a search engine.
Another big difference between the web and traditional well controlled collections
is that there is virtually no control over what people can put on the web. Couple
this flexibility to publish anything with the enormous influence of search engines
to route traffic and companies which deliberately manipulating search engines for
profit become a serious problem. This problem that has not been addressed in
traditional closed information retrieval systems. Also, it is interesting to note that
metadata efforts have largely failed with web search engines, because any text on
the page which is not directly represented to the user is abused to manipulate
search engines. There are even numerous companies which specialize in
manipulating search engines for profit.

traditional closed information retrieval systems. Also, it is interesting to note that
metadata efforts have largely failed with web search engines, because any text on
the page which is not directly represented to the user is abused to manipulate
search engines. There are even numerous companies which specialize in
manipulating search engines for profit.

4 System Anatomy
First, we will provide a high level discussion of the architecture. Then, there is some
in-depth descriptions of important data structures. Finally, the major applications:
crawling, indexing, and searching will be examined in depth.

4.1 Google Architecture
Overview
In this section, we will give a
high level overview of how the
whole system works as
pictured in Figure 1. Further
sections will discuss the
applications and data
structures not mentioned in
this section. Most of Google is
implemented in C or C++ for
efficiency and can run in either
Solaris or Linux.
In Google, the web crawling
(downloading of web pages) is
done by several distributed
crawlers. There is a URLserver
that sends lists of URLs to be
fetched to the crawlers. The
web pages that are fetched are
Figure 1. High Level Google Architecture
then sent to the storeserver.
The storeserver then
compresses and stores the web pages into a repository. Every web page has an
associated ID number called a docID which is assigned whenever a new URL is
parsed out of a web page. The indexing function is performed by the indexer and
the sorter. The indexer performs a number of functions. It reads the repository,
uncompresses the documents, and parses them. Each document is converted into
a set of word occurrences called hits. The hits record the word, position in
document, an approximation of font size, and capitalization. The indexer
distributes these hits into a set of "barrels", creating a partially sorted forward
index. The indexer performs another important function. It parses out all the links
in every web page and stores important information about them in an anchors file.
This file contains enough information to determine where each link points from
and to, and the text of the link.
The URLresolver reads the anchors file and converts relative URLs into absolute
URLs and in turn into docIDs. It puts the anchor text into the
http://www7.scu.edu.au/programme/fullpapers/1921/com1921.htm forward index, 8
associated with the docID that the anchor points to. It also generates a database
of links which are pairs of docIDs. The links database is used to compute
PageRanks for all the documents.

The URLresolver reads the anchors file and converts relative URLs into absolute
URLs and in turn into docIDs. It puts the anchor text into the forward index,
associated with the docID that the anchor points to. It also generates a database
of links which are pairs of docIDs. The links database is used to compute
PageRanks for all the documents.
The sorter takes the barrels, which are sorted by docID (this is a simplification, see
Section 4.2.5), and resorts them by wordID to generate the inverted index. This is
done in place so that little temporary space is needed for this operation. The sorter
also produces a list of wordIDs and offsets into the inverted index. A program
called DumpLexicon takes this list together with the lexicon produced by the
indexer and generates a new lexicon to be used by the searcher. The searcher is
run by a web server and uses the lexicon built by DumpLexicon together with the
inverted index and the PageRanks to answer queries.
4.2 Major Data Structures
Google's data structures are optimized so that a large document collection can be
crawled, indexed, and searched with little cost. Although, CPUs and bulk input
output rates have improved dramatically over the years, a disk seek still requires
about 10 ms to complete. Google is designed to avoid disk seeks whenever
possible, and this has had a considerable influence on the design of the data
structures.
4.2.1 BigFiles
BigFiles are virtual files spanning multiple file systems and are addressable by 64 bit
integers. The allocation among multiple file systems is handled automatically. The
BigFiles package also handles allocation and deallocation of file descriptors, since
the operating systems do not provide enough for our needs. BigFiles also support
rudimentary compression options.
4.2.2 Repository

The repository contains the full
HTML of every web page. Each
page is compressed using zlib (see
RFC1950). The choice of
compression technique is a
tradeoff between speed and
compression ratio. We chose zlib's
speed over a significant
improvement in compression Figure 2. Repository Data Structure
offered by bzip. The compression
rate of bzip was approximately 4 to 1 on the repository as compared to zlib's 3 to 1
compression. In the repository, the documents are stored one after the other and
are prefixed by docID, length, and URL as can be seen in Figure 2. The repository
requires no other data structures to be used in order to access it. This helps with 9
data consistency and makes development much easier; we can rebuild all the
other data structures from only the repository and a file which lists crawler errors.

. The compression
rate of bzip was approximately 4 to 1 on the repository as compared to zlib's 3 to 1
compression. In the repository, the documents are stored one after the other and
are prefixed by docID, length, and URL as can be seen in Figure 2. The repository
requires no other data structures to be used in order to access it. This helps with
data consistency and makes development much easier; we can rebuild all the
other data structures from only the repository and a file which lists crawler errors.
4.2.3 Document Index
The document index keeps information about each document. It is a fixed width
ISAM (Index sequential access mode) index, ordered by docID. The information
stored in each entry includes the current document status, a pointer into the
repository, a document checksum, and various statistics. If the document has been
crawled, it also contains a pointer into a variable width file called docinfo which
contains its URL and title. Otherwise the pointer points into the URLlist which
contains just the URL. This design decision was driven by the desire to have a
reasonably compact data structure, and the ability to fetch a record in one disk
seek during a search
Additionally, there is a file which is used to convert URLs into docIDs. It is a list of
URL checksums with their corresponding docIDs and is sorted by checksum. In
order to find the docID of a particular URL, the URL's checksum is computed and a
binary search is performed on the checksums file to find its docID. URLs may be
converted into docIDs in batch by doing a merge with this file. This is the
technique the URLresolver uses to turn URLs into docIDs. This batch mode of
update is crucial because otherwise we must perform one seek for every link
which assuming one disk would take more than a month for our 322 million link
dataset.
4.2.4 Lexicon
The lexicon has several different forms. One important change from earlier systems
is that the lexicon can fit in memory for a reasonable price. In the current
implementation we can keep the lexicon in memory on a machine with 256 MB of
main memory. The current lexicon contains 14 million words (though some rare
words were not added to the lexicon). It is implemented in two parts -- a list of the
words (concatenated together but separated by nulls) and a hash table of
pointers. For various functions, the list of words has some auxiliary information
which is beyond the scope of this paper to explain fully.
4.2.5 Hit Lists
A hit list corresponds to a list of occurrences of a particular word in a particular
document including position, font, and capitalization information. Hit lists account
for most of the space used in both the forward and the inverted indices. Because
of this, it is important to represent them as efficiently as possible. We considered
several alternatives for encoding position, font, and capitalization -- simple
encoding (a triple of integers), a compact encoding (a hand optimized allocation of
bits), and Huffman coding. In the end we chose a hand optimized compact
encoding since it required far less space than the simple encoding and far less bit
manipulation than Huffman coding. The details of the hits are shown in Figure 3. 10

of this, it is important to represent them as efficiently as possible. We considered
several alternatives for encoding position, font, and capitalization -- simple
encoding (a triple of integers), a compact encoding (a hand optimized allocation of
bits), and Huffman coding. In the end we chose a hand optimized compact
encoding since it required far less space than the simple encoding and far less bit
manipulation than Huffman coding. The details of the hits are shown in Figure 3.
Our compact encoding uses two bytes for every hit. There are two types of hits:
fancy hits and plain hits. Fancy hits include hits occurring in a URL, title, anchor
text, or meta tag. Plain hits include everything else. A plain hit consists of a
capitalization bit, font size, and 12 bits of word position in a document (all
positions higher than 4095 are labeled 4096). Font size is represented relative to
the rest of the document using three bits (only 7 values are actually used because
111 is the flag that signals a fancy hit). A fancy hit consists of a capitalization bit,
the font size set to 7 to indicate it is a fancy hit, 4 bits to encode the type of fancy
hit, and 8 bits of position. For anchor hits, the 8 bits of position are split into 4 bits
for position in anchor and 4 bits for a hash of the docID the anchor occurs in. This
gives us some limited phrase searching as long as there are not that many anchors
for a particular word. We expect to update the way that anchor hits are stored to
allow for greater resolution in the position and docIDhash fields. We use font size
relative to the rest of the document because when searching, you do not want to
rank otherwise identical documents differently just because one of the documents
is in a larger font.

The length of a hit list is stored
before the hits themselves. To
save space, the length of the hit
list is combined with the wordID
in the forward index and the
docID in the inverted index. This
limits it to 8 and 5 bits
respectively (there are some
tricks which allow 8 bits to be
borrowed from the wordID). If
the length is longer than would
fit in that many bits, an escape
code is used in those bits, and
the next two bytes contain the
actual length.
4.2.6 Forward Index
The forward index is actually
already partially sorted. It is Figure 3. Forward and Reverse Indexes and the
stored in a number of barrels Lexicon
(we used 64). Each barrel holds a
range of wordID's. If a
document contains words that fall into a particular barrel, the docID is recorded
into the barrel, followed by a list of wordID's with hitlists which correspond to
those words. This scheme requires slightly more storage because of duplicated
docIDs but the difference is very small for a reasonable number of buckets and 11
saves considerable time and coding complexity in the final indexing phase done by
the sorter. Furthermore, instead of storing actual wordID's, we store each wordID
as a relative difference from the minimum wordID that falls into the barrel the

(we used 64). Each barrel holds a
range of wordID's. If a
document contains words that fall into a particular barrel, the docID is recorded
into the barrel, followed by a list of wordID's with hitlists which correspond to
those words. This scheme requires slightly more storage because of duplicated
docIDs but the difference is very small for a reasonable number of buckets and
saves considerable time and coding complexity in the final indexing phase done by
the sorter. Furthermore, instead of storing actual wordID's, we store each wordID
as a relative difference from the minimum wordID that falls into the barrel the
wordID is in. This way, we can use just 24 bits for the wordID's in the unsorted
barrels, leaving 8 bits for the hit list length.
4.2.7 Inverted Index
The inverted index consists of the same barrels as the forward index, except that
they have been processed by the sorter. For every valid wordID, the lexicon
contains a pointer into the barrel that wordID falls into. It points to a doclist of
docID's together with their corresponding hit lists. This doclist represents all the
occurrences of that word in all documents.
An important issue is in what order the docID's should appear in the doclist. One
simple solution is to store them sorted by docID. This allows for quick merging of
different doclists for multiple word queries. Another option is to store them sorted
by a ranking of the occurrence of the word in each document. This makes
answering one word queries trivial and makes it likely that the answers to multiple
word queries are near the start. However, merging is much more difficult. Also, this
makes development much more difficult in that a change to the ranking function
requires a rebuild of the index. We chose a compromise between these options,
keeping two sets of inverted barrels -- one set for hit lists which include title or
anchor hits and another set for all hit lists. This way, we check the first set of
barrels first and if there are not enough matches within those barrels we check the
larger ones.
4.3 Crawling the Web
Running a web crawler is a challenging task. There are tricky performance and
reliability issues and even more importantly, there are social issues. Crawling is the
most fragile application since it involves interacting with hundreds of thousands of
web servers and various name servers which are all beyond the control of the
system.
In order to scale to hundreds of millions of web pages, Google has a fast
distributed crawling system. A single URLserver serves lists of URLs to a number of
crawlers (we typically ran about 3). Both the URLserver and the crawlers are
implemented in Python. Each crawler keeps roughly 300 connections open at
once. This is necessary to retrieve web pages at a fast enough pace. At peak
speeds, the system can crawl over 100 web pages per second using four crawlers.
This amounts to roughly 600K per second of data. A major performance stress is
DNS lookup. Each crawler maintains a its own DNS cache so it does not need to do
a DNS lookup before crawling each document. Each of the hundreds of
connections can be in a number of different states: looking up DNS, connecting to
host, sending request, and receiving response. These factors make the crawler a
complex component of the system. It uses asynchronous IO to manage events, 12
and a number of queues to move page fetches from state to state.

This amounts to roughly 600K per second of data. A major performance stress is
DNS lookup. Each crawler maintains a its own DNS cache so it does not need to do
a DNS lookup before crawling each document. Each of the hundreds of
connections can be in a number of different states: looking up DNS, connecting to
host, sending request, and receiving response. These factors make the crawler a
complex component of the system. It uses asynchronous IO to manage events,
and a number of queues to move page fetches from state to state.
It turns out that running a crawler which connects to more than half a million
servers, and generates tens of millions of log entries generates a fair amount of
email and phone calls. Because of the vast number of people coming on line, there
are always those who do not know what a crawler is, because this is the first one
they have seen. Almost daily, we receive an email something like, "Wow, you
looked at a lot of pages from my web site. How did you like it?" There are also some
people who do not know about the robots exclusion protocol, and think their
page should be protected from indexing by a statement like, "This page is
copyrighted and should not be indexed", which needless to say is difficult for web
crawlers to understand. Also, because of the huge amount of data involved,
unexpected things will happen. For example, our system tried to crawl an online
game. This resulted in lots of garbage messages in the middle of their game! It turns
out this was an easy problem to fix. But this problem had not come up until we had
downloaded tens of millions of pages. Because of the immense variation in web
pages and servers, it is virtually impossible to test a crawler without running it on
large part of the Internet. Invariably, there are hundreds of obscure problems
which may only occur on one page out of the whole web and cause the crawler to
crash, or worse, cause unpredictable or incorrect behavior. Systems which access
large parts of the Internet need to be designed to be very robust and carefully
tested. Since large complex systems such as crawlers will invariably cause
problems, there needs to be significant resources devoted to reading the email
and solving these problems as they come up.
4.4 Indexing the Web
Parsing -- Any parser which is designed to run on the entire Web must
handle a huge array of possible errors. These range from typos in HTML tags
to kilobytes of zeros in the middle of a tag, non-ASCII characters, HTML tags
nested hundreds deep, and a great variety of other errors that challenge
anyone's imagination to come up with equally creative ones. For maximum
speed, instead of using YACC to generate a CFG parser, we use flex to
generate a lexical analyzer which we outfit with its own stack. Developing
this parser which runs at a reasonable speed and is very robust involved a
fair amount of work.
Indexing Documents into Barrels -- After each document is parsed, it is
encoded into a number of barrels. Every word is converted into a wordID
by using an in-memory hash table -- the lexicon. New additions to the
lexicon hash table are logged to a file. Once the words are converted into
wordID's, their occurrences in the current document are translated into hit
lists and are written into the forward barrels. The main difficulty with
parallelization of the indexing phase is that the lexicon needs to be shared.
Instead of sharing the lexicon, we took the approach of writing a log of all
http://www7.scu.edu.au/programme/fullpapers/1921/com1921.htm we fixed at 14 million
the extra words that were not in a base lexicon, which 13
words. That way multiple indexers can run in parallel and then the small log
file of extra words can be processed by one final indexer.

lexicon hash table are logged to a file. Once the words are converted into
wordID's, their occurrences in the current document are translated into hit
lists and are written into the forward barrels. The main difficulty with
parallelization of the indexing phase is that the lexicon needs to be shared.
Instead of sharing the lexicon, we took the approach of writing a log of all
the extra words that were not in a base lexicon, which we fixed at 14 million
words. That way multiple indexers can run in parallel and then the small log
file of extra words can be processed by one final indexer.
Sorting -- In order to generate the inverted index, the sorter takes each of
the forward barrels and sorts it by wordID to produce an inverted barrel for
title and anchor hits and a full text inverted barrel. This process happens
one barrel at a time, thus requiring little temporary storage. Also, we
parallelize the sorting phase to use as many machines as we have simply by
running multiple sorters, which can process different buckets at the same
time. Since the barrels don't fit into main memory, the sorter further
subdivides them into baskets which do fit into memory based on wordID
and docID. Then the sorter, loads each basket into memory, sorts it and
writes its contents into the short inverted barrel and the full inverted barrel.

4.5 Searching
The goal of searching is to provide quality search results efficiently. Many of the
large commercial search engines seemed to have made great progress in terms of
efficiency. Therefore, we have focused more on quality of search in our research,
although we believe our solutions are scalable to commercial volumes with a bit
more effort. The google query evaluation process is show in Figure 4.

1. Parse the query.
To put a limit on response time, once a certain 2. Convert words into
number (currently 40,000) of matching wordIDs.
documents are found, the searcher 3. Seek to the start of the
automatically goes to step 8 in Figure 4. This doclist in the short
means that it is possible that sub-optimal results barrel for every word.
would be returned. We are currently 4. Scan through the
investigating other ways to solve this problem. doclists until there is a
In the past, we sorted the hits according to document that matches
PageRank, which seemed to improve the all the search terms.
situation. 5. Compute the rank of
4.5.1 The Ranking System that document for the
query.
Google maintains much more information about 6. If we are in the short
web documents than typical search engines. barrels and at the end of
Every hitlist includes position, font, and any doclist, seek to the
capitalization information. Additionally, we start of the doclist in the
factor in hits from anchor text and the PageRank full barrel for every word
of the document. Combining all of this and go to step 4.
information into a rank is difficult. We designed 7. If we are not at the end
our ranking function so that no particular factor of any doclist go to step
can have too much influence. First, consider the 4.
simplest case -- a single word query. In order to
rank a document with a single word query,
Google looks at that document's hit list for that
word. Google considers each hit to be one of

1. Parse the query.
To put a limit on response time, once a certain 2. Convert words into
number (currently 40,000) of matching wordIDs.
documents are found, the searcher 3. Seek to the start of the
automatically goes to step 8 in Figure 4. This doclist in the short
means that it is possible that sub-optimal results barrel for every word.
would be returned. We are currently 4. Scan through the
investigating other ways to solve this problem. doclists until there is a
In the past, we sorted the hits according to document that matches
PageRank, which seemed to improve the all the search terms.
situation. 5. Compute the rank of
4.5.1 The Ranking System that document for the
query.
Google maintains much more information about 6. If we are in the short
web documents than typical search engines. barrels and at the end of
Every hitlist includes position, font, and any doclist, seek to the
capitalization information. Additionally, we start of the doclist in the
factor in hits from anchor text and the PageRank full barrel for every word
of the document. Combining all of this and go to step 4.
information into a rank is difficult. We designed 7. If we are not at the end
our ranking function so that no particular factor of any doclist go to step
can have too much influence. First, consider the 4.
simplest case -- a single word query. In order to Sort the documents that
rank a document with a single word query, have matched by rank
Google looks at that document's hit list for that and return the top k.
word. Google considers each hit to be one of
several different types (title, anchor, URL, plain
text large font, plain text small font, ...), each of Figure 4. Google Query
which has its own type-weight. The type- Evaluation
weights make up a vector indexed by type.
Google counts the number of hits of each type in the hit list. Then every count is
converted into a count-weight. Count-weights increase linearly with counts at first
but quickly taper off so that more than a certain count will not help. We take the
dot product of the vector of count-weights with the vector of type-weights to
compute an IR score for the document. Finally, the IR score is combined with
PageRank to give a final rank to the document.
For a multi-word search, the situation is more complicated. Now multiple hit lists
must be scanned through at once so that hits occurring close together in a
document are weighted higher than hits occurring far apart. The hits from the
multiple hit lists are matched up so that nearby hits are matched together. For
every matched set of hits, a proximity is computed. The proximity is based on how
far apart the hits are in the document (or anchor) but is classified into 10 different
value "bins" ranging from a phrase match to "not even close". Counts are
computed not only for every type of hit but for every type and proximity. Every
http://www7.scu.edu.au/programme/fullpapers/1921/com1921.htm are converted into
type and proximity pair has a type-prox-weight. The counts 15
count-weights and we take the dot product of the count-weights and the type-
prox-weights to compute an IR score. All of these numbers and matrices can all be
displayed with the search results using a special debug mode. These displays have

multiple hit lists are matched up so that nearby hits are matched together. For
every matched set of hits, a proximity is computed. The proximity is based on how
far apart the hits are in the document (or anchor) but is classified into 10 different
value "bins" ranging from a phrase match to "not even close". Counts are
computed not only for every type of hit but for every type and proximity. Every
type and proximity pair has a type-prox-weight. The counts are converted into
count-weights and we take the dot product of the count-weights and the type-
prox-weights to compute an IR score. All of these numbers and matrices can all be
displayed with the search results using a special debug mode. These displays have
been very helpful in developing the ranking system.
4.5.2 Feedback
The ranking function has many parameters like the type-weights and the type-
prox-weights. Figuring out the right values for these parameters is something of a
black art. In order to do this, we have a user feedback mechanism in the search
engine. A trusted user may optionally evaluate all of the results that are returned.
This feedback is saved. Then when we modify the ranking function, we can see the
impact of this change on all previous searches which were ranked. Although far
from perfect, this gives us some idea of how a change in the ranking function
affects the search results.

5 Results and Performance

The most Query: bill clinton
important http://www.whitehouse.gov/
measure of 100.00% (no date) (0K)
a search http://www.whitehouse.gov/
engine is Office of the President
the quality 99.67% (Dec 23 1996) (2K)
of its search http://www.whitehouse.gov/WH/EOP/OP/html/OP_Home.html
results. Welcome To The White House
While a 99.98% (Nov 09 1997) (5K)
complete http://www.whitehouse.gov/WH/Welcome.html
user Send Electronic Mail to the President
evaluation 99.86% (Jul 14 1997) (5K)
is beyond http://www.whitehouse.gov/WH/Mail/html/Mail_President.html
the scope of mailto:president@whitehouse.gov
this paper, 99.98%
our own mailto:President@whitehouse.gov
experience 99.27%
with Google The "Unofficial" Bill Clinton
has shown 94.06% (Nov 11 1997) (14K)
it to http://zpub.com/un/un-bc.html
produce Bill Clinton Meets The Shrinks
better 86.27% (Jun 29 1997) (63K)
results than http://zpub.com/un/un-bc9.html
the major President Bill Clinton - The Dark Side
commercial 97.27% (Nov 10 1997) (15K)
search
engines for
most
searches. As

The most Query: bill clinton
important http://www.whitehouse.gov/
measure of 100.00% (no date) (0K)
a search http://www.whitehouse.gov/
engine is Office of the President
the quality 99.67% (Dec 23 1996) (2K)
of its search http://www.whitehouse.gov/WH/EOP/OP/html/OP_Home.html
results. Welcome To The White House
While a 99.98% (Nov 09 1997) (5K)
complete http://www.whitehouse.gov/WH/Welcome.html
user Send Electronic Mail to the President
evaluation 99.86% (Jul 14 1997) (5K)
is beyond http://www.whitehouse.gov/WH/Mail/html/Mail_President.html
the scope of mailto:president@whitehouse.gov
this paper, 99.98%
our own mailto:President@whitehouse.gov
experience 99.27%
with Google The "Unofficial" Bill Clinton
has shown 94.06% (Nov 11 1997) (14K)
it to http://zpub.com/un/un-bc.html
produce Bill Clinton Meets The Shrinks
better 86.27% (Jun 29 1997) (63K)
results than http://zpub.com/un/un-bc9.html
the major President Bill Clinton - The Dark Side
commercial 97.27% (Nov 10 1997) (15K)
search http://www.realchange.org/clinton.htm
engines for $3 Bill Clinton
most 94.73% (no date) (4K) http://www.gatewy.net/~tjohnson/clinton1.html
searches. As
an example
which Figure 4. Sample Results from Google
illustrates
the use of PageRank, anchor text, and proximity, Figure 4 shows Google's results
for a search on "bill clinton". These results demonstrates some of Google's features.
The results are clustered by server. This helps considerably when sifting through
result sets. A number of results are from the whitehouse.gov domain which is what
one may reasonably expect from such a search. Currently, most major commercial
search engines do not return any results from whitehouse.gov, much less the right
ones. Notice that there is no title for the first result. This is because it was not
crawled. Instead, Google relied on anchor text to determine this was a good
answer to the query. Similarly, the fifth result is an email address which, of course,
is not crawlable. It is also a result of anchor text.
All of the results are reasonably high quality pages and, at last check, none were
broken links. This is largely because they all have high PageRank. The PageRanks
are the percentages in red along with bar graphs. Finally, there are no results
about a Bill other than Clinton or about a Clinton other than Bill. This is because we
place heavy importance on the proximity of word occurrences. Of course a true
test of the quality of a search engine would involve an extensive user study or

All of the results are reasonably high quality pages and, at last check, none were
broken links. This is largely because they all have high PageRank. The PageRanks
are the percentages in red along with bar graphs. Finally, there are no results
about a Bill other than Clinton or about a Clinton other than Bill. This is because we
place heavy importance on the proximity of word occurrences. Of course a true
test of the quality of a search engine would involve an extensive user study or
results analysis which we do not have room for here. Instead, we invite the reader
to try Google for themselves at http://google.stanford.edu.
5.1 Storage Requirements
Aside from search quality, Google is designed to scale cost effectively to the size of
the Web as it grows. One aspect of this is to use storage efficiently. Table 1 has a
breakdown of some statistics and storage requirements of Google. Due to
compression the total size of the repository is about 53 GB, just over one third of
the total data it stores. At current disk prices this makes the repository a relatively
cheap source of useful data. More importantly, the total of all the data used by the
search engine requires a comparable amount of storage, about 55 GB.
Furthermore, most queries can be answered using just the short inverted index.
With better encoding and compression of the Document Index, a high quality web
search engine may fit onto a 7GB drive of a new PC.

Storage Statistics
5.2 System Performance Total Size of Fetched Pages 147.8 GB
It is important for a search engine Compressed Repository 53.5 GB
to crawl and index efficiently. This Short Inverted Index 4.1 GB
way information can be kept up
to date and major changes to the Full Inverted Index 37.2 GB
system can be tested relatively Lexicon 293 MB
quickly. For Google, the major
Temporary Anchor Data
operations are Crawling, Indexing, 6.6 GB
(not in total)
and Sorting. It is difficult to
measure how long crawling took Document Index Incl.
9.7 GB
overall because disks filled up, Variable Width Data
name servers crashed, or any Links Database 3.9 GB
number of other problems which
stopped the system. In total it Total Without Repository 55.2 GB
took roughly 9 days to download Total With Repository 108.7 GB
the 26 million pages (including
errors). However, once the system Web Page Statistics
was running smoothly, it ran Number of Web Pages Fetched 24 million
much faster, downloading the last
11 million pages in just 63 hours, Number of Urls Seen 76.5 million
averaging just over 4 million Number of Email Addresses 1.7 million
pages per day or 48.5 pages per
Number of 404's 1.6 million
second. We ran the indexer and
the crawler simultaneously. The Table 1. Statistics
indexer ran just faster than the
crawlers. This is largely because we spent just enough time optimizing the indexer
so that it would not be a bottleneck. These optimizations included bulk updates to
the document index and placement of critical data structures on the local disk. The

Storage Statistics
5.2 System Performance Total Size of Fetched Pages 147.8 GB
It is important for a search engine Compressed Repository 53.5 GB
to crawl and index efficiently. This Short Inverted Index 4.1 GB
way information can be kept up
to date and major changes to the Full Inverted Index 37.2 GB
system can be tested relatively Lexicon 293 MB
quickly. For Google, the major
Temporary Anchor Data
operations are Crawling, Indexing, 6.6 GB
(not in total)
and Sorting. It is difficult to
measure how long crawling took Document Index Incl.
9.7 GB
overall because disks filled up, Variable Width Data
name servers crashed, or any Links Database 3.9 GB
number of other problems which
stopped the system. In total it Total Without Repository 55.2 GB
took roughly 9 days to download Total With Repository 108.7 GB
the 26 million pages (including
errors). However, once the system Web Page Statistics
was running smoothly, it ran Number of Web Pages Fetched 24 million
much faster, downloading the last
11 million pages in just 63 hours, Number of Urls Seen 76.5 million
averaging just over 4 million Number of Email Addresses 1.7 million
pages per day or 48.5 pages per
Number of 404's 1.6 million
second. We ran the indexer and
the crawler simultaneously. The Table 1. Statistics
indexer ran just faster than the
crawlers. This is largely because we spent just enough time optimizing the indexer
so that it would not be a bottleneck. These optimizations included bulk updates to
the document index and placement of critical data structures on the local disk. The
indexer runs at roughly 54 pages per second. The sorters can be run completely in
parallel; using four machines, the whole process of sorting takes about 24 hours.
5.3 Search Performance
Improving the performance of search was not the major focus of our research up
to this point. The current version of Google answers most queries in between 1
and 10 seconds. This time is mostly dominated by disk IO over NFS (since disks are
spread over a number of machines). Furthermore, Google does not have any
optimizations such as query caching, subindices on common terms, and other
common optimizations. We intend to speed up Google considerably through
distribution and hardware, software, and algorithmic improvements. Our target is
to be able to handle several hundred queries per second. Table 2 has some sample
query times from the current version of Google. They are repeated to show the
speedups resulting from cached IO.

Same Query
Initial Query Repeated (IO
6 Conclusions mostly cached)
Google is designed to CPU Total CPU Total
be a scalable search Query
Time(s) Time(s) Time(s) Time(s)
engine. The primary
goal is to provide high al gore 0.09 2.13 0.06 0.06
quality search results vice
1.77 3.84 1.66 1.80
over a rapidly growing president
World Wide Web. hard
Google employs a 0.25 4.86 0.20 0.24
disks
number of techniques
to improve search search
1.31 9.63 1.16 1.16
quality including page engines
rank, anchor text, and Table 2. Search Times
proximity information.
Furthermore, Google is a complete architecture for gathering web pages, indexing
them, and performing search queries over them.
6.1 Future Work
A large-scale web search engine is a complex system and much remains to be
done. Our immediate goals are to improve search efficiency and to scale to
approximately 100 million web pages. Some simple improvements to efficiency
include query caching, smart disk allocation, and subindices. Another area which
requires much research is updates. We must have smart algorithms to decide what
old web pages should be recrawled and what new ones should be crawled. Work
toward this goal has been done in [Cho 98]. One promising area of research is
using proxy caches to build search databases, since they are demand driven. We
are planning to add simple features supported by commercial search engines like
boolean operators, negation, and stemming. However, other features are just
starting to be explored such as relevance feedback and clustering (Google
currently supports a simple hostname based clustering). We also plan to support
user context (like the user's location), and result summarization. We are also
working to extend the use of link structure and link text. Simple experiments
indicate PageRank can be personalized by increasing the weight of a user's home
page or bookmarks. As for link text, we are experimenting with using text
surrounding links in addition to the link text itself. A Web search engine is a very
rich environment for research ideas. We have far too many to list here so we do
not expect this Future Work section to become much shorter in the near future.
6.2 High Quality Search
The biggest problem facing users of web search engines today is the quality of the
results they get back. While the results are often amusing and expand users'
horizons, they are often frustrating and consume precious time. For example, the
top result for a search for "Bill Clinton" on one of the most popular commercial 20
search engines was the

The biggest problem facing users of web search engines today is the quality of the
results they get back. While the results are often amusing and expand users'
horizons, they are often frustrating and consume precious time. For example, the
top result for a search for "Bill Clinton" on one of the most popular commercial
search engines was the Bill Clinton Joke of the Day: April 14, 1997Google is
.
designed to provide higher quality search so as the Web continues to grow
rapidly, information can be found easily. In order to accomplish this Google makes
heavy use of hypertextual information consisting of link structure and link
(anchor) text. Google also uses proximity and font information. While evaluation of
a search engine is difficult, we have subjectively found that Google returns higher
quality search results than current commercial search engines. The analysis of link
structure via PageRank allows Google to evaluate the quality of web pages. The
use of link text as a description of what the link points to helps the search engine
return relevant (and to some degree high quality) results. Finally, the use of
proximity information helps increase relevance a great deal for many queries.
6.3 Scalable Architecture
Aside from the quality of search, Google is designed to scale. It must be efficient in
both space and time, and constant factors are very important when dealing with
the entire Web. In implementing Google, we have seen bottlenecks in CPU, memory
access, memory capacity, disk seeks, disk throughput, disk capacity, and network
IO. Google has evolved to overcome a number of these bottlenecks during various
operations. Google's major data structures make efficient use of available storage
space. Furthermore, the crawling, indexing, and sorting operations are efficient
enough to be able to build an index of a substantial portion of the web -- 24 million
pages, in less than one week. We expect to be able to build an index of 100 million
pages in less than a month.
6.4 A Research Tool
In addition to being a high quality search engine, Google is a research tool. The
data Google has collected has already resulted in many other papers submitted to
conferences and many more on the way. Recent research such as [Abiteboul 97]
has shown a number of limitations to queries about the Web that may be
answered without having the Web available locally. This means that Google (or a
similar system) is not only a valuable research tool but a necessary one for a wide
range of applications. We hope Google will be a resource for searchers and
researchers all around the world and will spark the next generation of search
engine technology.

7 Acknowledgments
Scott Hassan and Alan Steremberg have been critical to the development of
Google. Their talented contributions are irreplaceable, and the authors owe them
much gratitude. We would also like to thank Hector Garcia-Molina, Rajeev
Motwani, Jeff Ullman, and Terry Winograd and the whole WebBase group for their
support and insightful discussions. Finally we would like to recognize the
generous support of our equipment donors IBM, Intel, and Sun and our funders.
The research described here was conducted as part of the Stanford Integrated 21
Digital Library Project, supported by the National Science Foundation under
Cooperative Agreement IRI-9411306. Funding for this cooperative agreement is
also provided by DARPA and NASA, and by Interval Research, and the industrial

Google. Their talented contributions are irreplaceable, and the authors owe them
much gratitude. We would also like to thank Hector Garcia-Molina, Rajeev
Motwani, Jeff Ullman, and Terry Winograd and the whole WebBase group for their
support and insightful discussions. Finally we would like to recognize the
generous support of our equipment donors IBM, Intel, and Sun and our funders.
The research described here was conducted as part of the Stanford Integrated
Digital Library Project, supported by the National Science Foundation under
Cooperative Agreement IRI-9411306. Funding for this cooperative agreement is
also provided by DARPA and NASA, and by Interval Research, and the industrial
partners of the Stanford Digital Libraries Project.

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(WWW 97). Santa Clara, USA, April 7-11, 1997.

The Quest for Correct Information on the
Web: Hyper Search Engines. The Sixth International WWW Conference
(WWW 97). Santa Clara, USA, April 7-11, 1997.
[McBryan 94] Oliver A. McBryan. GENVL and WWWW: Tools for Taming the
Web. First International Conference on the World Wide Web. CERN, Geneva
(Switzerland), May 25-26-27 1994. http://www.cs.colorado.edu/home/
mcbryan/mypapers/www94.ps
[Page 98] Lawrence Page, Sergey Brin, Rajeev Motwani, Terry Winograd. The
PageRank Citation Ranking: Bringing Order to the Web. Manuscript in
progress. http://google.stanford.edu/~backrub/pageranksub.ps
[Pinkerton 94] Brian Pinkerton, Finding What People Want: Experiences with
the WebCrawler. The Second International WWW Conference Chicago, USA,
October 17-20, 1994.http://info.webcrawler.com/bp/WWW94.html
[Spertus 97] Ellen Spertus. ParaSite: Mining Structural Information on the
Web. The Sixth International WWW Conference (WWW 97). Santa Clara,
USA, April 7-11, 1997.
[TREC 96] Proceedings of the fifth Text REtrieval Conference (TREC-5).
Gaithersburg, Maryland, November 20-22, 1996. Publisher: Department of
Commerce, National Institute of Standards and Technology. Editors: D. K.
Harman and E. M. Voorhees. Full text at:http://trec.nist.gov/
[Witten 94] Ian H Witten, Alistair Moffat, and Timothy C. Bell.Managing
Gigabytes: Compressing and Indexing Documents and Images. New York:
Van Nostrand Reinhold, 1994.
[Weiss 96] Ron Weiss, Bienvenido Velez, Mark A. Sheldon, Chanathip
Manprempre, Peter Szilagyi, Andrzej Duda, and David K. Gifford.HyPursuit:
A Hierarchical Network Search Engine that Exploits Content-Link Hypertext
Clustering. Proceedings of the 7th ACM Conference on Hypertext. New
York, 1996.

Vitae
Sergey Brin received his B.S. degree in mathematics
and computer science from the University of
Maryland at College Park in 1993. Currently, he is a
Ph.D. candidate in computer science at Stanford
University where he received his M.S. in 1995. He is a
recipient of a National Science Foundation Graduate
Fellowship. His research interests include search
engines, information extraction from unstructured
sources, and data mining of large text collections and
scientific data.

Lawrence Page was born in East Lansing,
Michigan, and received a B.S.E. in Computer 23
Engineering at the University of Michigan Ann
Arbor in 1995. He is currently a Ph.D. candidate in
Computer Science at Stanford University. Some of

Sergey Brin received his B.S. degree in mathematics
and computer science from the University of
Maryland at College Park in 1993. Currently, he is a
Ph.D. candidate in computer science at Stanford
University where he received his M.S. in 1995. He is a
recipient of a National Science Foundation Graduate
Fellowship. His research interests include search
engines, information extraction from unstructured
sources, and data mining of large text collections and
scientific data.

Lawrence Page was born in East Lansing,
Michigan, and received a B.S.E. in Computer
Engineering at the University of Michigan Ann
Arbor in 1995. He is currently a Ph.D. candidate in
Computer Science at Stanford University. Some of
his research interests include the link structure of
the web, human computer interaction, search
engines, scalability of information access interfaces,
and personal data mining.

8 Appendix A: Advertising and Mixed Motives
Currently, the predominant business model for commercial search engines is
advertising. The goals of the advertising business model do not always correspond
to providing quality search to users. For example, in our prototype search engine
one of the top results for cellular phone is "The Effect of Cellular Phone Use Upon
Driver Attention", a study which explains in great detail the distractions and risk
associated with conversing on a cell phone while driving. This search result came
up first because of its high importance as judged by the PageRank algorithm, an
approximation of citation importance on the web [ age, 98]. It is clear that a
P
search engine which was taking money for showing cellular phone ads would
have difficulty justifying the page that our system returned to its paying
advertisers. For this type of reason and historical experience with other media
[Bagdikian 83], we expect that advertising funded search engines will be inherently
biased towards the advertisers and away from the needs of the consumers.
Since it is very difficult even for experts to evaluate search engines, search engine
bias is particularly insidious. A good example was OpenText, which was reported 24
to be selling companies the right to be listed at the top of the search results for
particular queries [

Since it is very difficult even for experts to evaluate search engines, search engine
bias is particularly insidious. A good example was OpenText, which was reported
to be selling companies the right to be listed at the top of the search results for
particular queries [Marchiori 97]. This type of bias is much more insidious than
advertising, because it is not clear who "deserves" to be there, and who is willing
to pay money to be listed. This business model resulted in an uproar, and
OpenText has ceased to be a viable search engine. But less blatant bias are likely to
be tolerated by the market. For example, a search engine could add a small factor
to search results from "friendly" companies, and subtract a factor from results from
competitors. This type of bias is very difficult to detect but could still have a
significant effect on the market. Furthermore, advertising income often provides
an incentive to provide poor quality search results. For example, we noticed a
major search engine would not return a large airline's homepage when the airline's
name was given as a query. It so happened that the airline had placed an
expensive ad, linked to the query that was its name. A better search engine would
not have required this ad, and possibly resulted in the loss of the revenue from the
airline to the search engine. In general, it could be argued from the consumer point
of view that the better the search engine is, the fewer advertisements will be
needed for the consumer to find what they want. This of course erodes the
advertising supported business model of the existing search engines. However,
there will always be money from advertisers who want a customer to switch
products, or have something that is genuinely new. But we believe the issue of
advertising causes enough mixed incentives that it is crucial to have a competitive
search engine that is transparent and in the academic realm.

9 Appendix B: Scalability
9. 1 Scalability of Google
We have designed Google to be scalable in the near term to a goal of 100 million
web pages. We have just received disk and machines to handle roughly that
amount. All of the time consuming parts of the system are parallelize and roughly
linear time. These include things like the crawlers, indexers, and sorters. We also
think that most of the data structures will deal gracefully with the expansion.
However, at 100 million web pages we will be very close up against all sorts of
operating system limits in the common operating systems (currently we run on
both Solaris and Linux). These include things like addressable memory, number of
open file descriptors, network sockets and bandwidth, and many others. We
believe expanding to a lot more than 100 million pages would greatly increase the
complexity of our system.
9.2 Scalability of Centralized Indexing Architectures
As the capabilities of computers increase, it becomes possible to index a very large
amount of text for a reasonable cost. Of course, other more bandwidth intensive
media such as video is likely to become more pervasive. But, because the cost of
production of text is low compared to media like video, text is likely to remain very
pervasive. Also, it is likely that soon we will have speech recognition that does a 25
reasonable job converting speech into text, expanding the amount of text
available. All of this provides amazing possibilities for centralized indexing. Here is

As the capabilities of computers increase, it becomes possible to index a very large
amount of text for a reasonable cost. Of course, other more bandwidth intensive
media such as video is likely to become more pervasive. But, because the cost of
production of text is low compared to media like video, text is likely to remain very
pervasive. Also, it is likely that soon we will have speech recognition that does a
reasonable job converting speech into text, expanding the amount of text
available. All of this provides amazing possibilities for centralized indexing. Here is
an illustrative example. We assume we want to index everything everyone in the
US has written for a year. We assume that there are 250 million people in the US
and they write an average of 10k per day. That works out to be about 850
terabytes. Also assume that indexing a terabyte can be done now for a reasonable
cost. We also assume that the indexing methods used over the text are linear, or
nearly linear in their complexity. Given all these assumptions we can compute how
long it would take before we could index our 850 terabytes for a reasonable cost
assuming certain growth factors. Moore's Law was defined in 1965 as a doubling
every 18 months in processor power. It has held remarkably true, not just for
processors, but for other important system parameters such as disk as well. If we
assume that Moore's law holds for the future, we need only 10 more doublings, or
15 years to reach our goal of indexing everything everyone in the US has written
for a year for a price that a small company could afford. Of course, hardware
experts are somewhat concerned Moore's Law may not continue to hold for the
next 15 years, but there are certainly a lot of interesting centralized applications
even if we only get part of the way to our hypothetical example.
Of course a distributed systems like Gl oss [Gravano 94] or Harvest will often be the
most efficient and elegant technical solution for indexing, but it seems difficult to
convince the world to use these systems because of the high administration costs
of setting up large numbers of installations. Of course, it is quite likely that
reducing the administration cost drastically is possible. If that happens, and
everyone starts running a distributed indexing system, searching would certainly
improve drastically.
Because humans can only type or speak a finite amount, and as computers
continue improving, text indexing will scale even better than it does now. Of
course there could be an infinite amount of machine generated content, but just
indexing huge amounts of human generated content seems tremendously useful.
So we are optimistic that our centralized web search engine architecture will
improve in its ability to cover the pertinent text information over time and that
there is a bright future for search.


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